01-Data Science Introduction

May 16, 2016

Books

An Introduction to Statistical Learning with Applications in R (ISLR)
- Focused on applying statistical learning
- Our primary book for the meetups
- Available at http://www-bcf.usc.edu/~gareth/ISL/
The Elements of Statistical Learning (ESL)
- Provides the theory
- We may use this for some deeper dives
- Available at http://statweb.stanford.edu/~tibs/ElemStatLearn/

Because ISLR!
I believe it's the best environment for learning the concepts of machine learning
R + RStudio are free and open source and easy to use
Concepts can be directly applied to other environments, particularly Python
We're all programmers and my experience tells me you won't have a problem with learning R!

Quantitative/continuous values
- Age
- Wage
Qualitative/categorical values
- Education level
- Stock movement (up, down)
- Low, medium, high (ordered categorical)

Method of Least Squares
- Legendre, Gauss beginning 19th century
- Basis of linear regression
- Predict quantitative values
Linear Discriminant Analysis
- Fisher, 1936
- Predict qualitative values
Logistic Regression
- Various, 1940's
- Alternative to LDA
Generalized Linear Models
- Nelder and Wedderburn, early 1970's

Many statistical learning methods are relevant and useful in a wide range of academic and non-academic disciplines, beyond just the statistical sciences
Statistical learning should not be viewed as a series of black boxes
While it is important to know what job is performed by each cog, it is not necessary to have the skills to construct the machine inside the box!
We presume that the reader is interested in applying statistical learning methods to real-world problems